Dr. Surdeanu works on natural language processing (NLP) systems that process and extract meaning from natural language texts such as question answering (answering natural language questions), information extraction (converting free text into structured relations and events), and textual entailment. He focuses mostly on interpretable models, i.e., approaches where the computer can explain in human understandable terms why it made a decision, and machine reasoning, i.e., methods that approximate the human capacity to understand bigger things from knowing smaller facts. He published more than 150 peer-reviewed articles, including four articles that were among the top three most cited articles at their respective venues that year. His work has been cited more than 19 thousand times, and has a current h-index of 44. Dr. Surdeanu’s work was funded by several United States government organizations (DARPA, NIH, NSF), as well as private foundations (the Allen Institute for Artificial Intelligence, the Bill Melinda Gates Foundation). Dr. Surdeanu also co-founded an NLP startup that applies these technologies to several domains including biomedical and agriculture.